Due to a lack of transparency in both algorithm and validation methodology, it is difficult for researchers and clinicians to select the appropriate tracker for their application. The aim of this work is to transparently present an adjustable physical activity classification algorithm that discriminates between dynamic, standing, and sedentary behavior. By means of easily adjustable parameters, the algorithm performance can be optimized for applications using different target populations and locations for tracker wear. Concerning an elderly target population with a tracker worn on the upper leg, the algorithm is optimized and validated under simulated free-living conditions. The fixed activity protocol (FAP) is performed by 20 participants; the simulated free-living protocol (SFP) involves another 20. Data segmentation window size and amount of physical activity threshold are optimized. The sensor orientation threshold does not vary. The validation of the algorithm is performed on 10 participants who perform the FAP and on 10 participants who perform the SFP. Percentage error (PE) and absolute percentage error (APE) are used to assess the algorithm performance. Standing and sedentary behavior are classified within acceptable limits (±10% error) both under fixed and simulated free-living conditions. Dynamic behavior is within acceptable limits under fixed conditions but has some limitations under simulated free-living conditions. We propose that this approach should be adopted by developers of activity trackers to facilitate the activity tracker selection process for researchers and clinicians. Furthermore, we are convinced that the adjustable algorithm potentially could contribute to the fast realization of new applications.

Low amounts of physical activity (PA) and prolonged periods of sedentary activity are common in hospitalized patients. Objective PA monitoring is needed to prevent the negative effects of inactivity, but a suitable algorithm is lacking. The aim of this study is to optimize and validate a classification algorithm that discriminates between sedentary, standing, and dynamic activities, and records postural transitions in hospitalized patients under free-living conditions. Optimization and validation in comparison to video analysis were performed in orthopedic and acutely hospitalized elderly patients with an accelerometer worn on the upper leg. Data segmentation window size (WS), amount of PA threshold (PA Th) and sensor orientation threshold (SO Th) were optimized in 25 patients, validation was performed in another 25. Sensitivity, specificity, accuracy, and (absolute) percentage error were used to assess the algorithm’s performance. Optimization resulted in the best performance with parameter settings: WS 4 s, PA Th 4.3 counts per second, SO Th 0.8 g. Validation showed that all activities were classified within acceptable limits (>80% sensitivity, specificity and accuracy, ±10% error), except for the classification of standing activity. As patients need to increase their PA and interrupt sedentary behavior, the algorithm is suitable for classifying PA in hospitalized patients.

Background/Objectives: Benign Paroxysmal Positioning Vertigo (BPPV), diagnosed in 46% of older adults with complaints of dizziness, causes movement-related vertigo. This case-control study compared physical activity, frailty and subjective well-being between older adults with BPPV (oaBPPV) and controls. Methods: Thirty-seven oaBPPV (mean age 73.13 (4.8)) were compared to 22 matched controls (mean age 73.5 (4.5)). Physical activity was measured using the MOX accelerometer and the International Physical Activity Questionnaire. Modified Fried criteria assessed frailty. The Dizziness Handicap Inventory, Falls Efficacy Scale, and 15-item Geriatric Depression Scale assessed subjective well-being. A post-hoc sub-analysis compared all variables compared between frail oaBPPV, robust oaBPPV and robust controls. Significance level was set at α = 0.05. Results: oaBPPV were significantly less physically active and were more (pre-)frail (p < 0.001) compared to controls. They performed significantly less active bouts (p = 0.002) and more sedentary bouts (p = 0.002), and a significantly different pattern of physical activity during the day. OaBPPV reported significantly less time in transportation activities (p = 0.003), leisure (p < 0.001), walking (p < 0.001) and moderate-intensity activities (p = 0.004) compared to controls. Frail oaBPPV were even less active (p = 0.01) and experienced more fear of falling (p < 0.001) and feelings of depression (p < 0.001) than robust oaBPPV and controls. Conclusions: BPPV can induce a vicious cycle of fear of provoking symptoms, decreased physical activity, well-being and consequently frailty. It is also possible that frail and less physically active older adults have an increased prevalence of BPPV. Post-treatment follow-up should assess BPPV, frailty and physical activity to determine if further rehabilitation is needed.

In this work, we present a new device to monitor the five main vital parameters of hospitalized patients: heart rate, respiratory rate, blood oxygen saturation, blood pressure and temperature. The device consists of one single unit placed on the chest with two electrodes connected to the patient. The device continuously acquires the electrocardiogram, 3-wavelength photoplethysmogram, bioimpedance, body temperature and 3-axis acceleration. These raw data are securely sent via a WiFi access point to a local server where algorithms are running to calculate the five vital parameters, combined with the level of activity and the posture. Alarms, live stream, and a real-time estimation of the early warning score used to assess the instantaneous state of a patient are available to nurses to react quickly and adequately if needed. Thanks to its small form factor and weight the device is worn unobtrusively by the patient who can move without being restrained by wires; a small battery ensures an autonomy of a few days. In this paper, the firmware and hardware architectures are detailed comprising the list of the sensors, the main chips used and the casing. The relationships between what the sensors measure and the vital parameters of interest are discussed.

Previous research showed that hallux rigidus (HR) affects foot and ankle kinematics during gait. It is unclear if HR affects lower limb kinematics as well. Does HR affect lower limb kinematics, and if so, is gait deviation correlated with patient-reported outcome? This was a retrospective case-control study, including 15 HR patients and 15 healthy controls who underwent three-dimensional gait analysis by using the Plug-in Gait lower body model. The Gait Profile Score (GPS), a gait index score describing gait deviation and composed out of nine Gait Variable Scores (GVS), and intersegmental range of motion of lower limb joints were assessed. Patient-reported outcome was assessed with the Foot Function Index (FFI) and Manchester-Oxford Foot Questionnaire (MOXFQ). Data were analysed with Student t-tests and Spearman rank correlations. HR significantly affects gait, reflected by a higher GPS in HR subjects as compared to healthy controls. Gait deviation was seen in ankle flexion (GVSankle flexion) and to a lesser extent in pelvic rotation (GVSpelvic rotation). Interestingly, these differences were not detected when lower limb kinematics were evaluated by comparing the intersegmental ranges of motion of these joints. Positive correlations were present between patient-reported outcomes and GPS, especially functional subdomains, were positively correlated with GPS and GVSankle flexion. This study demonstrated that HR, next to foot kinematics, additionally affects lower limb kinematics evaluated with an objective gait index score, i.e. GPS. The positive correlation between the GPS and patient-reported outcome can be seen as the first step in defining whether objectively measured gait indices can be used in considering surgery since most of the benefit of surgery will be expected in the patients with most gait deviation.

Background: DFNA9 is a genetic disease of the inner ear, causing progressive bilateral sensorineural deafness and bilateral vestibulopathy (BV). In this study, DizzyQuest, a mobile vestibular diary, and the MOX accelerometer were combined to assess the daily life functional limitations and physical activity of patients with DFNA9 suffering from BV. These parameters might be appropriate as potential candidacy criteria and outcome measures for new therapeutic interventions for BV. Methods: Fifteen DFNA9 patients with BV and twelve age-matched healthy controls were included. The DizzyQuest was applied for six consecutive days, which assessed the participants’ extent of functional limitations, tiredness, types of activities performed during the day, and type of activity during which the participant felt most limited. The MOX accelerometer was worn during the same six days of DizzyQuest use, measuring the participants intensity and type of physical activity. Mixed-effects linear and logistic regression analyses were performed to compare the DFNA9 patients and control group. Results: DFNA9 patients with BV felt significantly more limited in activities during the day compared to the age-matched controls, especially in social participation (p < 0.005). However, these reported limitations did not cause adjustment in the types of activities and did not reduce the intensity or type of physical activity measured with accelerometry. In addition, no relationships were found between self-reported functional limitations and physical activity. Conclusions: This study demonstrated that self-reported functional limitations are significantly higher among DFNA9 patients with BV. As a result, these limitations might be considered as part of the candidacy criteria or outcome measures for therapeutic interventions. In addition, the intensity or type of physical activity performed during the day need to be addressed more specifically in future research.